[scikit-learn] Fitting Lognormal Distribution
Startup Hire
blrstartuphire at gmail.com
Thu May 26 03:23:31 EDT 2016
Hi,
(1) - Thanks. will do that
(2) - I am fitting the distribution for 2 different set of values.. I will
find the distribution as mentioned by you in (1).. But, now having 2
curves, how do i find the meetings point(s) ?
Regards,
Sanant
On Thu, May 26, 2016 at 12:16 PM, federico vaggi <vaggi.federico at gmail.com>
wrote:
> 1) The normal distribution is parametrized by standard deviation and
> mean. Simply take the mean and standard deviation of the log of your
> values?
>
> 2) Which curves? You only mentioned a single log normal distribution.
>
> On Thu, 26 May 2016 at 08:42 Startup Hire <blrstartuphire at gmail.com>
> wrote:
>
>> Hi Michael,
>>
>> :)
>>
>>
>> (1) - I think you are right, how do I fit a normal distribution to the
>> log of values?
>>
>> (2) Intersection ---> Meeting point (s) . as in where the curves cross
>> each other (it can be in multiple places too!)
>>
>>
>> Regards,
>> Sanant
>>
>> On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg <
>> michael.eickenberg at gmail.com> wrote:
>>
>>> Hi Sanant,
>>>
>>> On Thursday, May 26, 2016, Startup Hire <blrstartuphire at gmail.com>
>>> wrote:
>>>
>>>> Hi all,
>>>>
>>>> Hope you are doing good.
>>>>
>>>
>>> I would like to think so, but you never know where ML will lead us ...
>>>
>>>
>>>>
>>>> I am working on a project where I need to do the following things:
>>>>
>>>> 1. I need to fit a lognormal distribution to a set of values [I know
>>>> its lognormal by a simple XY scatter plot in excel]
>>>>
>>>
>>> if your distribution is lognormal, why don't you try fitting a gaussian
>>> to the log of the values? is this too unstable?
>>>
>>>
>>>>
>>>> 2. I need to find the intersection of the lognormal distribution so
>>>> that I can decide cut-off values based on that.
>>>>
>>>
>>> what exactly do you mean by intersection?
>>>
>>>
>>>>
>>>>
>>>> Can you guide me on (1) and (2) can be achieved in python?
>>>>
>>>> Regards,
>>>> Sanant
>>>>
>>>
>>>
>>> Michael
>>>
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